Remaining useful life prediction of rolling bearings based on deep transfer learning

To address the problem of low prediction accuracy due to insufficient training samples of bearing remaining useful life (RUL) prediction model, a rolling bearing RUL prediction method based on deep transfer learning is proposed. First, the original vibration signal is used by the deep belief network (DBN) and the self-organizing mapping neural network (SOM) to construct the bearing health factor (HI). Then, prediction model is trained based on the LSTM model by the shared hidden layer transfer method. Finally, LSTM-DT is used to predict RUL value. The experiment results show that the constructed HI can accurately reflect the health state of the bearing, LSTM-DT algorithm can effectively improve the accuracy of RUL prediction.